CN112053558A - Traffic jam state identification method, device and equipment - Google Patents
Traffic jam state identification method, device and equipment Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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Abstract
The invention provides a traffic jam state identification method, a device and equipment, wherein the method comprises the following steps: acquiring a detection data set acquired by an electronic police, and screening corresponding vehicle running data in the detection data set according to different attributes; calculating actual travel time according to the vehicle travel data, and deleting abnormal vehicle travel data exceeding a specified travel time threshold value to obtain corresponding vehicle travel data; and judging the congestion state of the road and evaluating the effect of trunk line coordination according to the vehicle running data. By utilizing the method disclosed by the invention, the multi-source electric alarm data can be fully utilized, and unreasonable abnormal vehicle running data can be screened out according to the vehicle travel time, so that the traffic state analysis is carried out based on more accurate vehicle running data, more accurate congestion state identification results are obtained, and the optimization effect of trunk line coordination is better.
Description
Technical Field
The invention relates to the technical field of traffic, in particular to a method, a device and equipment for identifying a traffic jam state.
Background
The traffic flow and the travel time are the most concerned traffic information of traffic managers and travelers, and comprehensively reflect the traffic running state. In the construction process of intelligent traffic projects, the processes of traffic jam state identification, traffic problem diagnosis, optimization effect comparison and the like also depend on traffic flow data and travel time data to the utmost extent.
However, the current traffic data acquisition mode has serious difference in various places and low utilization rate of effective data. In the prior art, common data acquisition forms comprise detectors, internet data, manual research and the like, but detection equipment such as geomagnetism, coils and the like is high in construction and maintenance cost and low in equipment integrity rate; the data of the internet track vehicle is less, the data is miscellaneous and the accuracy is low; a plurality of servers need to be deployed in a professional big data system, so that the construction and maintenance cost is high; manual investigation is time consuming and labor intensive.
The electric police are widely applied to urban traffic management, a large amount of valuable information is contained in massive electric police vehicle passing data, and an effective technical means is urgently needed to mine the travel rule in electric police characteristic data.
Disclosure of Invention
The invention provides a traffic jam state identification method, a device and equipment, which solve the problems of high cost, complex means and low utilization rate of effective data of the current scheme for acquiring traffic data by mining vehicle driving data in electric warning data.
In a first aspect, the present invention provides a method for traffic congestion status identification, the method comprising:
acquiring a detection data set acquired by an electronic police, and screening corresponding vehicle running data in the detection data set according to different attributes;
calculating actual travel time according to the vehicle travel data, and deleting abnormal vehicle travel data exceeding a specified travel time threshold value to obtain corresponding vehicle travel data;
and judging the congestion state of the road and evaluating the effect of trunk line coordination according to the vehicle running data.
Optionally, evaluating the effect of trunk coordination based on the vehicle travel data comprises:
determining the time deviation additionally consumed by the vehicle subjected to road interference in unit distances of different road sections, wherein the road sections with different design speeds correspond to different time deviations;
determining a calibration travel time according to road sections with different design speeds, and obtaining a theoretical travel time interval according to the difference value between the calibration travel time and the corresponding time deviation;
determining the parking times of the vehicles on the road section according to the actual travel time and the theoretical travel time interval of the vehicles on different road sections;
and carrying out weighted summation on the parking times of all the road sections according to the proportion of the parking times of the vehicles of all the road sections in the parking times of the main road sections to obtain the accumulated average parking times of the main road.
Optionally, calculating an actual travel time from the vehicle travel data comprises:
acquiring vehicle driving data of each vehicle passing through a starting intersection and a finishing intersection of each road section within a set sampling time interval;
matching the vehicles passing through the initial intersection with the vehicles passing through the terminal intersection to obtain vehicle driving data which are matched with each other;
and obtaining the actual travel time of the vehicle according to the time when the vehicle passes through the initial intersection and the final intersection respectively in the matched vehicle driving data.
Optionally, the prescribed time of flight threshold is determined as follows:
calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line and the highest speed limit value of the road section;
and calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line, the lowest speed limit value of the road section and the influence value of the signal control intersection of the road section on the vehicle running time.
Optionally, the determining the congestion state of the road according to the vehicle driving data includes:
acquiring the average travel speed of the vehicle on the road section according to the road section length of each road section and the actual travel time of the vehicle in the road section within a set sampling time interval;
obtaining an accumulated average speed according to the average travel speed in n continuous set sampling time intervals before the current time;
and comparing the accumulated average speed with the grade division standard of the urban road traffic state to obtain the congestion state of the road.
Optionally, in the detection data set, screening corresponding vehicle driving data according to different attributes includes:
determining attributes needing to be screened, and determining key fields corresponding to the attributes;
and screening the electric alarm data with different formats in the detection data set by using the key fields corresponding to the attributes to obtain corresponding vehicle driving data.
Optionally, the detection data set includes, but is not limited to: license plate number, vehicle color, vehicle type, acquisition time, acquisition place, vehicle driving direction and vehicle driving lane.
In a second aspect, the present invention provides an apparatus for traffic congestion status identification, comprising a memory and a processor, wherein:
the memory is used for storing a computer program;
the processor is used for reading the program in the memory and executing the following steps:
acquiring a detection data set acquired by an electronic police, and screening corresponding vehicle running data in the detection data set according to different attributes;
calculating actual travel time according to the vehicle travel data, and deleting abnormal vehicle travel data exceeding a specified travel time threshold value to obtain corresponding vehicle travel data;
and judging the congestion state of the road and evaluating the effect of trunk line coordination according to the vehicle running data.
Optionally, the processor evaluates the effect of trunk coordination based on the vehicle travel data, including:
determining the time deviation additionally consumed by the vehicle subjected to road interference in unit distances of different road sections, wherein the road sections with different design speeds correspond to different time deviations;
determining a calibration travel time according to road sections with different design speeds, and obtaining a theoretical travel time interval according to the difference value between the calibration travel time and the corresponding time deviation;
determining the parking times of the vehicles on the road section according to the actual travel time and the theoretical travel time interval of the vehicles on different road sections;
and carrying out weighted summation on the parking times of all the road sections according to the proportion of the parking times of the vehicles of all the road sections in the parking times of the main road sections to obtain the accumulated average parking times of the main road.
Optionally, the processor calculates an actual travel time from the vehicle travel data, including:
acquiring vehicle driving data of each vehicle passing through a starting intersection and a finishing intersection of each road section within a set sampling time interval;
matching the vehicles passing through the initial intersection with the vehicles passing through the terminal intersection to obtain vehicle driving data which are matched with each other;
and obtaining the actual travel time of the vehicle according to the time when the vehicle passes through the initial intersection and the final intersection respectively in the matched vehicle driving data.
Optionally, the prescribed time of flight threshold is determined as follows:
calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line and the highest speed limit value of the road section;
and calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line, the lowest speed limit value of the road section and the influence value of the signal control intersection of the road section on the vehicle running time.
Optionally, the processor determines the congestion state of the road according to the vehicle driving data, and includes:
acquiring the average travel speed of the vehicle on the road section according to the road section length of each road section and the actual travel time of the vehicle in the road section within a set sampling time interval;
obtaining an accumulated average speed according to the average travel speed in n continuous set sampling time intervals before the current time;
and comparing the accumulated average speed with the grade division standard of the urban road traffic state to obtain the congestion state of the road.
Optionally, in the detection data set, the processor filters corresponding vehicle driving data according to different attributes, including:
determining attributes needing to be screened, and determining key fields corresponding to the attributes;
and screening the electric alarm data with different formats in the detection data set by using the key fields corresponding to the attributes to obtain corresponding vehicle driving data.
Optionally, the detection data set includes, but is not limited to: license plate number, vehicle color, vehicle type, acquisition time, acquisition place, vehicle driving direction and vehicle driving lane.
In a third aspect, the present invention provides an apparatus for identifying a traffic congestion state, comprising:
the system comprises an attribute screening unit, a data processing unit and a data processing unit, wherein the attribute screening unit is used for acquiring a detection data set acquired by an electronic police, and screening corresponding vehicle driving data in the detection data set according to different attributes;
the abnormal data deleting unit is used for calculating the actual travel time according to the vehicle travel data, deleting the abnormal vehicle travel data exceeding the specified travel time threshold value and obtaining the corresponding vehicle travel data;
and the judging and evaluating unit is used for judging the congestion state of the road and evaluating the effect of trunk line coordination according to the vehicle running data.
Optionally, the evaluation unit evaluates the effect of trunk coordination according to the vehicle travel data, and includes:
determining the time deviation additionally consumed by the vehicle subjected to road interference in unit distances of different road sections, wherein the road sections with different design speeds correspond to different time deviations;
determining a calibration travel time according to road sections with different design speeds, and obtaining a theoretical travel time interval according to the difference value between the calibration travel time and the corresponding time deviation;
determining the parking times of the vehicles on the road section according to the actual travel time and the theoretical travel time interval of the vehicles on different road sections;
and carrying out weighted summation on the parking times of all the road sections according to the proportion of the parking times of the vehicles of all the road sections in the parking times of the main road sections to obtain the accumulated average parking times of the main road.
Optionally, the abnormal data deleting unit calculates an actual travel time according to the vehicle travel data, and includes:
acquiring vehicle driving data of each vehicle passing through a starting intersection and a finishing intersection of each road section within a set sampling time interval;
matching the vehicles passing through the initial intersection with the vehicles passing through the terminal intersection to obtain vehicle driving data which are matched with each other;
and obtaining the actual travel time of the vehicle according to the time when the vehicle passes through the initial intersection and the final intersection respectively in the matched vehicle driving data.
Optionally, the prescribed time of flight threshold is determined as follows:
calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line and the highest speed limit value of the road section;
and calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line, the lowest speed limit value of the road section and the influence value of the signal control intersection of the road section on the vehicle running time.
Optionally, the determining and evaluating unit determines the congestion state of the road according to the vehicle traveling data, and includes:
acquiring the average travel speed of the vehicle on the road section according to the road section length of each road section and the actual travel time of the vehicle in the road section within a set sampling time interval;
obtaining an accumulated average speed according to the average travel speed in n continuous set sampling time intervals before the current time;
and comparing the accumulated average speed with the grade division standard of the urban road traffic state to obtain the congestion state of the road.
Optionally, in the detection data set, the attribute filtering unit filters corresponding vehicle driving data according to different attributes, and includes:
determining attributes needing to be screened, and determining key fields corresponding to the attributes;
and screening the electric alarm data with different formats in the detection data set by using the key fields corresponding to the attributes to obtain corresponding vehicle driving data.
Optionally, the detection data set includes, but is not limited to: license plate number, vehicle color, vehicle type, acquisition time, acquisition place, vehicle driving direction and vehicle driving lane.
In a fourth aspect, the present invention provides a computer program medium having stored thereon a computer program which, when executed by a processor, performs the steps of the title as provided in the first aspect above.
The method, the device and the equipment for identifying the traffic jam state have the following beneficial effects that:
the multi-source electric alarm data can be fully utilized, after the relevant vehicle running data are obtained based on the electric alarm data, unreasonable abnormal vehicle running data can be screened out according to the vehicle travel time, so that traffic state analysis is carried out based on more accurate vehicle running data, more accurate congestion state recognition results are obtained, and the optimization effect of main line coordination is better.
Drawings
Fig. 1 is a flowchart of a method for identifying a traffic congestion status according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a layout of an electrical alarm according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electrical alarm characteristic data acquisition according to an embodiment of the present invention;
FIG. 4 is a flowchart of a vehicle travel time calculation and data screening provided by an embodiment of the present invention;
fig. 5 is a schematic diagram illustrating a layout requirement of a multi-intersection electrical alarm according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an apparatus for identifying a traffic congestion state according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a traffic congestion status recognition apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Several traffic data processing schemes provided in the prior art are as follows:
a traffic travel track extraction method based on electric alarm data comprises the steps of firstly obtaining point position data and electric alarm data of electric alarm equipment after preprocessing, then extracting a travel path of a vehicle according to the point position data and the electric alarm data of the electric alarm equipment after preprocessing, then classifying the travel path of the vehicle according to the running speed of the vehicle and the travel time of the travel path, and finally determining the traffic travel track of the vehicle according to the travel path of the vehicle after classification.
According to the scheme, the traffic travel track of the vehicle is extracted on the basis of the point position data and the electric alarm data of the electric alarm equipment, then the travel paths are classified, and different traffic travel track determination methods are adopted according to the travel paths of different categories. However, in an actual scene, different vehicle types have different acceleration and deceleration speeds and different running speeds, and partial areas, such as beijing, have policy influence on the vehicle passing time period and the passing route.
The invention provides a traffic jam state identification method, which is characterized in that after a detection data set is collected, based on travel time calculation of identification of different vehicle types and license plate numbers and an abnormal data screening algorithm, the running rules of vehicles of different vehicle types are subjected to cluster analysis, so that the threshold value of the vehicle is determined, whether the travel time data of the same license plate number is in the threshold value range is pre-judged, the data accuracy, the calculation speed and the analysis precision are improved, and the method is more favorable for exploring the travel rule of a traffic participant and assisting in decision of traffic management and control.
The method is mainly characterized by deeply excavating mass electric alarm travel time sequence change data, extracting vehicle running state parameters through a fuzzy recognition algorithm, automatically matching and calculating road section travel time parameters, and further judging the road congestion state. Frequent occurrence and accidental occurrence of congestion nodes are identified, and fine division of intersection time periods is assisted through cluster analysis of electric police vehicle passing data.
Example 1
Fig. 1 is a flowchart of a traffic congestion status identification method according to an embodiment of the present invention, including:
step S101, a detection data set collected by an electronic police is obtained, and corresponding vehicle driving data are screened according to different attributes in the detection data set;
step S102, calculating actual travel time according to the vehicle travel data, and deleting abnormal vehicle travel data exceeding a specified travel time threshold value to obtain corresponding vehicle travel data;
and step S103, judging the congestion state of the road and evaluating the effect of the trunk line coordination according to the vehicle running data.
Specifically, the electronic police integrated bayonet function is arranged on the entrance way of each direction of a road intersection, and the function of slapping when meeting a vehicle can be realized. Fig. 2 is a schematic layout diagram of an electric police according to an embodiment of the present invention, and the electric police is generally disposed at a position 18-23m away from a stop line, and may be disposed by means of a signal light pole.
Optionally, the detection data set includes, but is not limited to: license plate number, vehicle color, vehicle type, acquisition time, acquisition place, vehicle driving direction and vehicle driving lane.
Optionally, in the detection data set, screening corresponding vehicle driving data according to different attributes includes:
determining attributes needing to be screened, and determining key fields corresponding to the attributes;
and screening the electric alarm data with different formats in the detection data set by using the key fields corresponding to the attributes to obtain corresponding vehicle driving data.
The data formats of the electric police output by different electronic police for the same meaning information at present may be different, for example, the driving direction may be represented by "north-south" and "south-north", the driving lane may be represented by "1", "1 st lane" and "first lane", and the like. In order to obtain more comprehensive electric alarm information, the electric alarm data in different formats are classified and stored according to the meaning of the representation.
As an optional implementation manner, determining attributes to be screened, where the attributes to be screened may be one or more, and the embodiment of the present invention supports multi-attribute screening search, for example, attributes of a local vehicle and a foreign vehicle, a small vehicle and other vehicles, different time periods, and the like may be simultaneously screened; because the speed difference between the small-sized automobile and the large-sized automobile is large, the embodiment of the invention supports the separate screening of the travel time data of the small-sized automobile and other vehicles.
As an optional implementation manner, determining a key field corresponding to the attribute, screening out corresponding vehicle driving data by using fuzzy recognition of the key field, and automatically screening out data of north-south and south-north if the key field is input to north-south.
Fig. 3 is a schematic diagram illustrating screening of vehicle driving data meeting conditions based on attributes, where the attributes may include, but are not limited to, a date, a license plate number, a vehicle type, a driving direction, and a time period, each attribute field has a corresponding keyword, fuzzy matching is performed using the corresponding keyword, and corresponding vehicle driving data is output, and the embodiment of the present invention supports multi-attribute and multi-keyword detection, such as distinguishing between a local vehicle and a foreign vehicle, a small vehicle and other vehicles, and between different time periods, for example, inputting keywords "lub", "small vehicle", "north-south", "8: 00-9: 00', automatically screening all the vehicle driving data of which the point position meets the condition.
As an optional implementation manner, the screening effect of screening the corresponding vehicle driving data according to the different attributes is evaluated by the recall ratio RE and the accuracy ratio AP;
the recall ratio RE refers to the percentage of attribute-related data to be screened and all related data in a database, and reflects the comprehensiveness of a screening result;
the recall RE may be expressed as:
in the formula, RT represents the related data of the attribute to be retrieved, and DB represents all the related data in the database.
The accuracy rate AP refers to the percentage of effective data after abnormal data are removed according to the screening rule and data related to the attribute to be screened, and reflects the accuracy of the screening result.
The accuracy AP can be expressed as:
in the formula, ST represents valid data from which abnormal data is removed.
Optionally, calculating an actual travel time from the vehicle travel data comprises:
acquiring vehicle driving data of each vehicle passing through a starting intersection and a finishing intersection of each road section within a set sampling time interval;
matching the vehicles passing through the initial intersection with the vehicles passing through the terminal intersection to obtain vehicle driving data which are matched with each other;
and obtaining the actual travel time of the vehicle according to the time when the vehicle passes through the initial intersection and the final intersection respectively in the matched vehicle driving data.
As an alternative implementation, the principle of calculating the actual travel time according to the electrical alarm characteristic data is as follows: when the vehicle passes through the stop line, the electric police can shoot and recognize the license plate number of the passing vehicle and record the time of the passing vehicle through the stop line. And matching the vehicle passing information extracted from the initial intersection and the final intersection, and extracting the time difference between the time when the vehicle with the same license plate number passes through the final intersection and the initial intersection so as to extract the travel time of the vehicle.
The determination of the same vehicle is not limited to the same license plate number, and any technique that can determine that the vehicles are the same may be applied to the embodiment of the present invention.
As shown in table 1, which is a calculation principle of travel time provided by the embodiment of the present invention, a vehicle with license plate number of lube B12345 12 at 2019/1/1: 11: 43 by the initial intersection, i.e., intersection 1, at 12 on day 2019/1/1: 22: 58 through the end intersection, i.e., intersection 2, the actual travel time of the vehicle may be calculated to be 11 minutes and 15 seconds.
TABLE 1 calculation principle of travel time
As an alternative embodiment, the abnormal vehicle travel data exceeding the predetermined travel time threshold is deleted, and the corresponding vehicle travel data is obtained.
The abnormal vehicle travel data is filtered out according to the predetermined travel time threshold, so that the validity of the corresponding vehicle travel data is relatively high, and the effects of judging the road congestion state and evaluating the trunk coordination according to the corresponding vehicle travel data are better and the validity is higher.
The electric alarm detects the fault and the abnormal running vehicle, such as the driver path selection behavior and the parking behavior, and abnormal vehicle running data exist in the extracted travel time; in addition, abnormal vehicle running data caused by a scene exists, the same vehicle passes through the same road section for multiple times in one day, and the passing time of different trips is matched in the actual travel time calculation process, so that part of the travel time is far greater than the actual value. In order to reduce the occurrence of the situation as much as possible, the data is processed, and the abnormal vehicle running data is removed so that the obtained data can be applied to actual research and service.
As an alternative embodiment, the reason for the generation of unreasonable data is as follows:
reasons for the smaller travel time include any one or more of the following: the driver does not obey the traffic rule to drive at an overspeed; travel of special vehicles, such as ambulances, fire engines, and the like; the electric alarm identifies a mistake.
Reasons for greater travel time include any one or more of the following: the driver walks around; stopping in midway; multiple trips are carried out; the electric alarm identifies a mistake.
Optionally, the prescribed time of flight threshold is determined as follows:
calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line and the highest speed limit value of the road section;
and calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line, the lowest speed limit value of the road section and the influence value of the signal control intersection of the road section on the vehicle running time.
As an alternative embodiment, a minimum value T of the travel time is specifiedijminThe calculation of (2):
under normal conditions, the travel time of the vehicle is not lower than that of the vehicle on the road section under the condition of free flow or speed limit, and the minimum travel time calculation formula is as follows:
wherein L represents the length of a path between the installation positions of i and j electrical alarms, v1Indicating the road speed limit. When the road section does not have the definite speed limit standard, the values are designed according to the designed speeds of all levels of roads, and the designed speeds of all levels of roads are shown in the table 2.
TABLE 2 road design speed values at various levels
As an alternative, a maximum travel time T is specifiedijmaxThe calculation of (2):
the maximum travel time can be understood as the maximum travel time for a vehicle to travel at the lowest speed and stop at each signal control intersection in a red light after passing a certain road section, and the maximum travel time is calculated according to the following formula:
in the formula, L represents the length of a path between the installation positions of the i and j electric alarms; v. of2The lowest speed limit value of the road is shown, and generally 10-30km/h can be taken; n represents the number of the signal control intersections of the road section;
the average value of the signal period of each intersection in the time period is shown, and the value range is generally 60-160 s.
As an alternative embodiment, calculating the actual travel time from the vehicle travel data includes:
acquiring vehicle driving data of each vehicle passing through a starting intersection and a finishing intersection of each road section within a set sampling time interval;
matching the vehicles passing through the initial intersection with the vehicles passing through the terminal intersection to obtain vehicle driving data which are matched with each other;
and obtaining the actual travel time of the vehicle according to the time when the vehicle passes through the initial intersection and the final intersection respectively in the matched vehicle driving data.
As shown in fig. 4, the flow of calculating the vehicle travel time and screening the data is specifically as follows:
step S401, vehicle driving data of a stop line at a starting intersection of a selected time interval, namely X, is extracted from the vehicle driving datai＝(Xi1,Xi2,...,Xij,...,Xin)；
Wherein: n total number of passing vehicles within a selected time interval; xijFor the information that the jth vehicle in the phase passes through the terminal intersection in a period, let alphajNumber plate information, beta, representing the jth vehiclejPassing time information representing the jth vehicle, namely:
Xij＝(αj,βj)，Xj∈X； (5)
in step S402, X 'is extracted from the vehicle running data as the vehicle running data of the stop line at the selected time interval end point intersection'i＝(X′i1,X′i2,...,X′ij,...,X′in)；
Step S403, according to the vehicle information X of the first vehicle at the starting point intersectioni1Set of vehicle information X at intersection with terminali' matching, assuming that the matched vehicle information is Si1，Si1＝(ai1,βi1')；
Step S404, according to Xi1And Si1Extracting the travel time T of the first vehicle in the phasei1Namely:
Ti1＝βi1-βi1'； (6)
step S405, matching the second vehicle, the third vehicle, … and the nth vehicle in sequence according to the same method, and calculating corresponding travel time Ti2、Ti3，…，Tin；
Step S406, obtaining travel time data T according to the specified travel time threshold valuei1，Ti2，…，TinScreening is carried out;
and deleting abnormal vehicle running data exceeding the specified travel time threshold value, namely deleting travel time smaller than the minimum value of the specified travel time threshold value or larger than the maximum value of the specified travel time threshold value.
In step S407, the processed travel time is output.
Optionally, the determining the congestion state of the road according to the vehicle driving data includes:
acquiring the average travel speed of the vehicle on the road section according to the road section length of each road section and the actual travel time of the vehicle in the road section within a set sampling time interval;
obtaining an accumulated average speed according to the average travel speed in n continuous set sampling time intervals before the current time;
and comparing the accumulated average speed with the grade division standard of the urban road traffic state to obtain the congestion state of the road.
As an alternative embodiment, the average travel speed of the vehicle in each road segment is determined according to the road segment length of each road segment and the actual travel time of the vehicle in the road segment within the set sampling time interval. The schematic diagram of the layout requirement of the multi-intersection electric alarm provided by the embodiment of the invention is shown in fig. 5.
The method can be used for extracting and calculating the travel time of the vehicle passing data between the electric alarms AB and BC at two adjacent intersections respectively by taking five minutes as an interval, and preliminarily screening the travel time data by utilizing a vehicle speed threshold.
It should be noted that the five-minute time interval is only a specific example of the selected time interval, and is not a limitation to the selected time interval, and the time interval may be selected according to specific traffic conditions in specific implementation.
The average travel speed of the road section in the set sampling time interval is obtained by the following formula
In the above formula, n represents the number of vehicles obtained by matching the data of the two electric alarms on the road section AB, L represents the distance between the two electric alarms on the road section AB,
indicating the ith retrieved vehicle travel time for the road segment AB.
As an optional implementation manner, in order to more intuitively and accurately show the road congestion change rule, on the basis of the average speed of the road obtained in five minutes, the change of the accumulated average speed is calculated by using the average speed of the road in fifteen minutes, namely three road in five minutes, as a basis for judging the congestion state of the road, and table 3 shows a calculation manner of the accumulated average speed.
The idea of calculating the cumulative average speed is to obtain the cumulative average speed from the average travel speed in n consecutive set sampling time intervals before the current time, where the current time is, for example, 10: when n is 3, taking 10: 00-10: 05. 10: 05-10: 10. 10: 10-10: 15, and calculating to obtain the accumulated average speed. The value of n may be any value greater than 1 according to specific traffic conditions, and the fifteen minutes is only an example of the case where n is 3, and the calculation of the cumulative average speed is not particularly limited.
TABLE 3 calculation of cumulative average velocity
As an optional implementation manner, the urban road traffic state classification standard is as follows: according to relevant regulations in an urban road traffic management evaluation index system published by the ministry of public security 2012 in China, the degree of traffic congestion can be described by the average travel speed of motor vehicles, the traffic state is blurred into 5 traffic states of clear, basically clear, crowded, congested and blocked according to the level of urban roads and the average travel speed threshold of road sections, and the specific division standard is shown in table 4.
TABLE 4 urban road traffic status level division Standard
Optionally, evaluating the effect of trunk coordination based on the vehicle travel data comprises:
determining the time deviation additionally consumed by the vehicle subjected to road interference in unit distances of different road sections, wherein the road sections with different design speeds correspond to different time deviations;
determining a calibration travel time according to road sections with different design speeds, and obtaining a theoretical travel time interval according to the difference value between the calibration travel time and the corresponding time deviation;
determining the parking times of the vehicles on the road section according to the actual travel time and the theoretical travel time interval of the vehicles on different road sections;
and carrying out weighted summation on the parking times of all the road sections according to the proportion of the parking times of the vehicles of all the road sections in the parking times of the main road sections to obtain the accumulated average parking times of the main road.
As an alternative embodiment, when a vehicle runs on a road section, the travel speed is often less than the design speed due to interference of signal lamps, adjacent vehicles, pedestrians, non-motor vehicles and the like, that is, the travel time is generally long, but the deviation degree of the actual travel time and the design speed travel time is large, and no relevant measurement standard exists. And introducing a parameter deviation rho to represent the time additionally consumed by the vehicle subjected to road interference in a unit distance, wherein the value of rho is related to the design speed of the road section and the peak and peak leveling conditions of the road section.
As an alternative embodiment, the deviation is calibrated by using actual inspection travel time of peak and flat peaks of more than 40 main roads in agalman city, and the calibration result is shown in table 5:
TABLE 5 deviation calibration of actual travel time and designed vehicle speed travel time of urban road
As an alternative embodiment, the principle of using the vehicle travel data to evaluate the trunk coordination effect is as follows: the method comprises the steps of calculating to obtain a theoretical travel time interval by calibrating the deviation of actual travel time of a road and designed vehicle speed travel time, reversely deducing the parking times of vehicles on the road section by combining the actual travel time, obtaining the accumulated average parking times of a main line according to the proportion condition of the actual travel time interval, objectively displaying a coordination optimization effect, and enhancing persuasion, wherein the specific table is shown in table 6.
TABLE 6 cumulative average number of stops for urban road trunks
As an optional implementation manner, according to the proportion of the vehicle parking times of each road section in the parking times of the main line section, performing weighted summation on the parking times of each road section, establishing a cumulative average parking time w model, and further evaluating the main line coordination effect as follows:
TABLE 7 evaluation of urban road trunk coordination effect
(wherein n is the number of the signal control intersections between two electric alarms (including the starting and ending points), and n is more than or equal to 2)
Specifically, when the urban road trunk line coordination effect is evaluated to be good or better, the trunk line coordination effect can be accepted without changing;
when the urban road trunk line coordination effect is evaluated to be general or poor, the trunk line coordination effect is not acceptable, and trunk line coordination needs to be repeated.
Example 2
An embodiment of the present invention provides an apparatus 600 for identifying a traffic congestion state, which includes a memory 601 and a processor 602, as shown in fig. 6, where:
the memory is used for storing a computer program;
the processor is used for reading the program in the memory and executing the following steps:
acquiring a detection data set acquired by an electronic police, and screening corresponding vehicle running data in the detection data set according to different attributes;
calculating actual travel time according to the vehicle travel data, and deleting abnormal vehicle travel data exceeding a specified travel time threshold value to obtain corresponding vehicle travel data;
and judging the congestion state of the road and evaluating the effect of trunk line coordination according to the vehicle running data.
Optionally, the processor evaluates the effect of trunk coordination based on the vehicle travel data, including:
determining the time deviation additionally consumed by the vehicle subjected to road interference in unit distances of different road sections, wherein the road sections with different design speeds correspond to different time deviations;
determining a calibration travel time according to road sections with different design speeds, and obtaining a theoretical travel time interval according to the difference value between the calibration travel time and the corresponding time deviation;
determining the parking times of the vehicles on the road section according to the actual travel time and the theoretical travel time interval of the vehicles on different road sections;
and carrying out weighted summation on the parking times of all the road sections according to the proportion of the parking times of the vehicles of all the road sections in the parking times of the main road sections to obtain the accumulated average parking times of the main road.
Optionally, the processor calculates an actual travel time from the vehicle travel data, including:
acquiring vehicle driving data of each vehicle passing through a starting intersection and a finishing intersection of each road section within a set sampling time interval;
matching the vehicles passing through the initial intersection with the vehicles passing through the terminal intersection to obtain vehicle driving data which are matched with each other;
and obtaining the actual travel time of the vehicle according to the time when the vehicle passes through the initial intersection and the final intersection respectively in the matched vehicle driving data.
Optionally, the prescribed time of flight threshold is determined as follows:
calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line and the highest speed limit value of the road section;
and calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line, the lowest speed limit value of the road section and the influence value of the signal control intersection of the road section on the vehicle running time.
Optionally, the processor determines the congestion state of the road according to the vehicle driving data, and includes:
acquiring the average travel speed of the vehicle on the road section according to the road section length of each road section and the actual travel time of the vehicle in the road section within a set sampling time interval;
obtaining an accumulated average speed according to the average travel speed in n continuous set sampling time intervals before the current time;
and comparing the accumulated average speed with the grade division standard of the urban road traffic state to obtain the congestion state of the road.
Optionally, in the detection data set, the processor filters corresponding vehicle driving data according to different attributes, including:
determining attributes needing to be screened, and determining key fields corresponding to the attributes;
and screening the electric alarm data with different formats in the detection data set by using the key fields corresponding to the attributes to obtain corresponding vehicle driving data.
Optionally, the detection data set includes, but is not limited to: license plate number, vehicle color, vehicle type, acquisition time, acquisition place, vehicle driving direction and vehicle driving lane.
An embodiment of the present invention provides a device for identifying a traffic congestion state, as shown in fig. 7, including:
the attribute screening unit 701 is used for acquiring a detection data set acquired by an electronic police, and screening corresponding vehicle driving data in the detection data set according to different attributes;
an abnormal data deleting unit 702, configured to calculate an actual travel time according to the vehicle travel data, and delete abnormal vehicle travel data exceeding a specified travel time threshold to obtain corresponding vehicle travel data;
and a determination and evaluation unit 703 for determining a congestion state of the road and evaluating an effect of trunk coordination according to the vehicle travel data.
Optionally, the evaluation unit evaluates the effect of trunk coordination according to the vehicle travel data, and includes:
determining the time deviation additionally consumed by the vehicle subjected to road interference in unit distances of different road sections, wherein the road sections with different design speeds correspond to different time deviations;
determining a calibration travel time according to road sections with different design speeds, and obtaining a theoretical travel time interval according to the difference value between the calibration travel time and the corresponding time deviation;
determining the parking times of the vehicles on the road section according to the actual travel time and the theoretical travel time interval of the vehicles on different road sections;
and carrying out weighted summation on the parking times of all the road sections according to the proportion of the parking times of the vehicles of all the road sections in the parking times of the main road sections to obtain the accumulated average parking times of the main road.
Optionally, the abnormal data deleting unit calculates an actual travel time according to the vehicle travel data, and includes:
acquiring vehicle driving data of each vehicle passing through a starting intersection and a finishing intersection of each road section within a set sampling time interval;
matching the vehicles passing through the initial intersection with the vehicles passing through the terminal intersection to obtain vehicle driving data which are matched with each other;
and obtaining the actual travel time of the vehicle according to the time when the vehicle passes through the initial intersection and the final intersection respectively in the matched vehicle driving data.
Optionally, the prescribed time of flight threshold is determined as follows:
calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line and the highest speed limit value of the road section;
and calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line, the lowest speed limit value of the road section and the influence value of the signal control intersection of the road section on the vehicle running time.
Optionally, the determining and evaluating unit determines the congestion state of the road according to the vehicle traveling data, and includes:
acquiring the average travel speed of the vehicle on the road section according to the road section length of each road section and the actual travel time of the vehicle in the road section within a set sampling time interval;
obtaining an accumulated average speed according to the average travel speed in n continuous set sampling time intervals before the current time;
and comparing the accumulated average speed with the grade division standard of the urban road traffic state to obtain the congestion state of the road.
Optionally, in the detection data set, the attribute filtering unit filters corresponding vehicle driving data according to different attributes, and includes:
determining attributes needing to be screened, and determining key fields corresponding to the attributes;
and screening the electric alarm data with different formats in the detection data set by using the key fields corresponding to the attributes to obtain corresponding vehicle driving data.
Optionally, the detection data set includes, but is not limited to: license plate number, vehicle color, vehicle type, acquisition time, acquisition place, vehicle driving direction and vehicle driving lane.
The present invention also provides a computer program medium having a computer program stored thereon, which when executed by a processor, implements the steps of the data transmission time domain parameter indication method applied to the user equipment UE provided in the above embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The technical solutions provided by the present application are introduced in detail, and the present application applies specific examples to explain the principles and embodiments of the present application, and the descriptions of the above examples are only used to help understand the method and the core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A traffic congestion state identification method is characterized by comprising the following steps:
acquiring a detection data set acquired by an electronic police, and screening corresponding vehicle running data in the detection data set according to different attributes;
calculating actual travel time according to the vehicle travel data, and deleting abnormal vehicle travel data exceeding a specified travel time threshold value to obtain corresponding vehicle travel data;
and judging the congestion state of the road and evaluating the effect of trunk line coordination according to the vehicle running data.
2. The method of claim 1, wherein evaluating the effect of trunk coordination based on the vehicle travel data comprises:
determining the time deviation additionally consumed by the vehicle subjected to road interference in unit distances of different road sections, wherein the road sections with different design speeds correspond to different time deviations;
determining a calibration travel time according to road sections with different design speeds, and obtaining a theoretical travel time interval according to the difference value between the calibration travel time and the corresponding time deviation;
determining the parking times of the vehicles on the road section according to the actual travel time and the theoretical travel time interval of the vehicles on different road sections;
and carrying out weighted summation on the parking times of all the road sections according to the proportion of the parking times of the vehicles of all the road sections in the parking times of the main road sections to obtain the accumulated average parking times of the main road.
3. The method of claim 1, wherein calculating an actual travel time from the vehicle travel data comprises:
acquiring vehicle driving data of each vehicle passing through a starting intersection and a finishing intersection of each road section within a set sampling time interval;
matching the vehicles passing through the initial intersection with the vehicles passing through the terminal intersection to obtain vehicle driving data which are matched with each other;
and obtaining the actual travel time of the vehicle according to the time when the vehicle passes through the initial intersection and the final intersection respectively in the matched vehicle driving data.
4. The method of claim 1, wherein the prescribed time-of-flight threshold is determined as follows:
calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line and the highest speed limit value of the road section;
and calculating the minimum value of the specified travel time according to the length of the road section between the starting intersection and the ending intersection of the trunk line, the lowest speed limit value of the road section and the influence value of the signal control intersection of the road section on the vehicle running time.
5. The method of claim 1, wherein determining the congestion status of the road based on the vehicle travel data comprises:
acquiring the average travel speed of the vehicle on the road section according to the road section length of each road section and the actual travel time of the vehicle in the road section within a set sampling time interval;
obtaining an accumulated average speed according to the average travel speed in n continuous set sampling time intervals before the current time;
and comparing the accumulated average speed with the grade division standard of the urban road traffic state to obtain the congestion state of the road.
6. The method of claim 1, wherein screening the corresponding vehicle travel data in the test data set according to different attributes comprises:
determining attributes needing to be screened, and determining key fields corresponding to the attributes;
and screening the electric alarm data with different formats in the detection data set by using the key fields corresponding to the attributes to obtain corresponding vehicle driving data.
7. The method of claim 1, wherein the detection data set includes, but is not limited to: license plate number, vehicle color, vehicle type, acquisition time, acquisition place, vehicle driving direction and vehicle driving lane.
8. An apparatus for traffic congestion status identification, comprising:
the system comprises an attribute screening unit, a data processing unit and a data processing unit, wherein the attribute screening unit is used for acquiring a detection data set acquired by an electronic police, and screening corresponding vehicle driving data in the detection data set according to different attributes;
the abnormal data deleting unit is used for calculating the actual travel time according to the vehicle travel data, deleting the abnormal vehicle travel data exceeding the specified travel time threshold value and obtaining the corresponding vehicle travel data;
and the judging and evaluating unit is used for judging the congestion state of the road and evaluating the effect of trunk line coordination according to the vehicle running data.
9. An apparatus for traffic congestion status identification, comprising a memory and a processor, wherein:
the memory is used for storing a computer program;
the processor is used for reading the program in the memory and executing the method for identifying the traffic jam state as claimed in any one of claims 1 to 7.
10. A computer program medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112581764A (en) * | 2020-12-11 | 2021-03-30 | 广州海云捷讯科技有限公司 | Centralized control method, system, terminal and storage medium based on big data |
| CN112907945A (en) * | 2021-01-18 | 2021-06-04 | 阿里巴巴集团控股有限公司 | Road state determination and navigation route planning method and equipment |
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| CN113487866A (en) * | 2021-06-30 | 2021-10-08 | 重庆电子工程职业学院 | Traffic management system based on multi-source heterogeneous data fusion technology |
| CN113947925A (en) * | 2021-10-20 | 2022-01-18 | 上海应用技术大学 | Green wave evaluation method, device and equipment based on electronic police data |
| CN114040442A (en) * | 2021-11-26 | 2022-02-11 | 珠海格力电器股份有限公司 | Wireless gateway transmission system, information filtering method thereof and wireless gateway |
| CN114373310A (en) * | 2022-01-19 | 2022-04-19 | 京东方科技集团股份有限公司 | Index data processing method and device for vehicle driving road section and electronic equipment |
| CN114999150A (en) * | 2022-05-23 | 2022-09-02 | 雄狮汽车科技(南京)有限公司 | Road section congestion judging method and device, vehicle and storage medium |
| CN115019525A (en) * | 2022-06-20 | 2022-09-06 | 杭州海康威视数字技术股份有限公司 | Travel time data screening method and traffic signal control method |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002049947A (en) * | 2000-08-01 | 2002-02-15 | Yazaki Corp | Congestion degree display system and congestion degree calculation method |
| CN104281738A (en) * | 2014-08-06 | 2015-01-14 | 青岛海信网络科技股份有限公司 | Evaluation system and method for trunk line coordination control scheme of trunk line road |
| CN104615897A (en) * | 2015-02-13 | 2015-05-13 | 北方工业大学 | Road section travel time estimation method based on low-frequency GPS data |
| CN105336183A (en) * | 2015-10-26 | 2016-02-17 | 青岛海信网络科技股份有限公司 | Traffic congestion control method and device based on road section passing capacity |
| CN105869396A (en) * | 2016-04-28 | 2016-08-17 | 泰华智慧产业集团股份有限公司 | Vehicle crossing index statistical method and system based on big data platform |
| CN107578630A (en) * | 2017-09-11 | 2018-01-12 | 合肥工业大学 | The method to set up that a kind of road grade crossing time great distance is drawn |
| CN108615362A (en) * | 2018-06-22 | 2018-10-02 | 广东交通职业技术学院 | A kind of road traffic flow parameter extracting method under 5G car networkings environment |
| CN109147330A (en) * | 2018-09-19 | 2019-01-04 | 青岛海信网络科技股份有限公司 | A kind of congestion recognition methods and device |
| CN109189760A (en) * | 2018-08-16 | 2019-01-11 | 北京易华录信息技术股份有限公司 | A kind of building of traffic element portrait and analysis method based on big data technology |
| CN111435463A (en) * | 2019-01-11 | 2020-07-21 | 华为技术有限公司 | Data processing method and related equipment and system |
-
2020
- 2020-08-25 CN CN202010863358.2A patent/CN112053558A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002049947A (en) * | 2000-08-01 | 2002-02-15 | Yazaki Corp | Congestion degree display system and congestion degree calculation method |
| CN104281738A (en) * | 2014-08-06 | 2015-01-14 | 青岛海信网络科技股份有限公司 | Evaluation system and method for trunk line coordination control scheme of trunk line road |
| CN104615897A (en) * | 2015-02-13 | 2015-05-13 | 北方工业大学 | Road section travel time estimation method based on low-frequency GPS data |
| CN105336183A (en) * | 2015-10-26 | 2016-02-17 | 青岛海信网络科技股份有限公司 | Traffic congestion control method and device based on road section passing capacity |
| CN105869396A (en) * | 2016-04-28 | 2016-08-17 | 泰华智慧产业集团股份有限公司 | Vehicle crossing index statistical method and system based on big data platform |
| CN107578630A (en) * | 2017-09-11 | 2018-01-12 | 合肥工业大学 | The method to set up that a kind of road grade crossing time great distance is drawn |
| CN108615362A (en) * | 2018-06-22 | 2018-10-02 | 广东交通职业技术学院 | A kind of road traffic flow parameter extracting method under 5G car networkings environment |
| CN109189760A (en) * | 2018-08-16 | 2019-01-11 | 北京易华录信息技术股份有限公司 | A kind of building of traffic element portrait and analysis method based on big data technology |
| CN109147330A (en) * | 2018-09-19 | 2019-01-04 | 青岛海信网络科技股份有限公司 | A kind of congestion recognition methods and device |
| CN111435463A (en) * | 2019-01-11 | 2020-07-21 | 华为技术有限公司 | Data processing method and related equipment and system |
Non-Patent Citations (2)
| Title |
|---|
| 曾民族等: "《知识技术及其应用》", 30 November 2015 * |
| 董明等: "《大数据基础与应用》", 31 January 2018 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112581764A (en) * | 2020-12-11 | 2021-03-30 | 广州海云捷讯科技有限公司 | Centralized control method, system, terminal and storage medium based on big data |
| CN112907945A (en) * | 2021-01-18 | 2021-06-04 | 阿里巴巴集团控股有限公司 | Road state determination and navigation route planning method and equipment |
| CN113379233A (en) * | 2021-06-08 | 2021-09-10 | 重庆大学 | Travel time reliability estimation method and device based on high-order moment |
| CN113379233B (en) * | 2021-06-08 | 2023-02-28 | 重庆大学 | Travel time reliability estimation method and device based on high-order moment |
| CN113487866A (en) * | 2021-06-30 | 2021-10-08 | 重庆电子工程职业学院 | Traffic management system based on multi-source heterogeneous data fusion technology |
| CN113947925A (en) * | 2021-10-20 | 2022-01-18 | 上海应用技术大学 | Green wave evaluation method, device and equipment based on electronic police data |
| CN114040442A (en) * | 2021-11-26 | 2022-02-11 | 珠海格力电器股份有限公司 | Wireless gateway transmission system, information filtering method thereof and wireless gateway |
| CN114040442B (en) * | 2021-11-26 | 2023-11-14 | 珠海格力电器股份有限公司 | Wireless gateway transmission system, information filtering method thereof and wireless gateway |
| CN114373310A (en) * | 2022-01-19 | 2022-04-19 | 京东方科技集团股份有限公司 | Index data processing method and device for vehicle driving road section and electronic equipment |
| CN114999150A (en) * | 2022-05-23 | 2022-09-02 | 雄狮汽车科技(南京)有限公司 | Road section congestion judging method and device, vehicle and storage medium |
| CN115019525A (en) * | 2022-06-20 | 2022-09-06 | 杭州海康威视数字技术股份有限公司 | Travel time data screening method and traffic signal control method |
| CN115019525B (en) * | 2022-06-20 | 2024-06-11 | 杭州海康威视数字技术股份有限公司 | Screening method of travel time data and traffic signal control method |
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